Purrr
Purrr is an R package that provides a set of tools for working with functional programming in R. It makes it easy to map functions over vectors, lists, and other objects, apply functions repeatedly, and use other techniques common in functional programming languages.
Purrr: Functional Programming Tools for R
Purrr is an R package that provides a set of tools for working with functional programming in R. It makes it easy to map functions over vectors, lists, and other objects, apply functions repeatedly, and use other techniques common in functional programming languages.
What is Purrr?
Purrr is an R package designed to make functional programming in R easier and more consistent. It provides a set of tools for working with functions and vectors in R that makes code easier to read, write, and understand.
Some of the key features of Purrr include:
- map functions to easily apply a function over vectors, lists, and other objects
- map variants like map_lgl(), map_int(), map_dbl() for mapping to specific output types
- safely evaluate expressions using possibly() and safely()
- compose multiple simple functions together with compose()
- apply a function recursively over data structures with reduce(), accumulate(), and related functions
- create custom pipes with pipe() to improve code flow
- extract elements from vectors and lists with extract functions
- and many other functional programming helpers
By using the consistent syntax of purrr, you can avoid lots of repetitive code for iteration, error handling, and data extraction/transformation in R. This helps make code faster to prototype and easier to read. It's a core part of the tidyverse collection of packages.
Purrr Features
Features
- Map functions over vectors, lists and data frames
- Apply functions repeatedly
- Write functions that take and return other functions
- Use lambda functions
- Compose multiple simple functions into a new function
- Partial application of functions
Pricing
- Open Source
Pros
Cleaner and more readable code
Avoid nested for loops
Easier to think functionally
Powerful abstraction and reuse
Cons
Can be harder to debug
Functional programming has a learning curve
Might not integrate well with some traditional R workflows
Reviews & Ratings
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